Good Programmer, Bad Programmer

3 min readJul 12, 2019


The use of denominations like “good” or “bad” related to programmers and programming carries with it a thin separation between these two perceptional poles that represent the end results of the programming process, reflecting the quality of the code delivered, respectively the quality of a programmer’s effort and behavior as a whole. This means that the usage of the two denominations is often contextual, “good” and “bad” being moving points on a imaginary value scale with a wide range of values within and outside the interval determined by the two.

The “good programmer” label is a idealization of the traits associated with being a programmer - analyzing and understanding the requirements, filling the gaps when necessary, translating the requirements in robust designs, developing quality code with a minimum of overwork, delivering on-time, being able to help others, to work as part of a (self-organizing) team and alone, when the project requires it, to follow methodologies, processes or best practices, etc. The problem with such a definition is that there’s no fix limit, considering that programmer’s job description can include an extensive range of requirements.

The “bad programmer” label is used in general when programmers (repeatedly) fail to reach others’ expectations, occasionally the labeling being done independently of one’s experience in the field. The volume of bugs and mistakes, the fuzziness of designs and of the code written, the lack of comments and documentation, the lack of adherence to methodologies, processes, best practices and naming conventions are often considered as indicators for such labels. Sometimes even the smallest mistakes or the wrong perceptions of one’s effort and abilities can trigger such labels.

Labeling people as “good” or “bad” has the tendency of reinforcing one’s initial perception, in extremis leading to self-fulfilling prophecies - predictions that directly or indirectly cause themselves to become true, by the very terms on how the predictions came into being. Thus, when somebody labels another as “good” or “bad” he more likely will look for signs that reinforce his previous believes. This leads to situations in which “good” programmers’ mistakes are easier overlooked than “bad” programmers’ mistakes, even if the mistakes are similar.

A good label can in theory motivate, while a bad label can easily demotivate, though their effects depend from person to person. Such labels can easily become a problem for beginners, because they can easily affect beginners’ perception about themselves. It’s so easy to forget that programming is a continuous learning process in which knowledge is relative and highly contextual, each person having strengths and weaknesses.

Each programmer has a particular set of skills that differentiate him from other programmers. Each programmer is unique, aspect reflected in the code one writes. Expecting programmers to fit an ideal pattern is unrealistic. Instead of using labels one should attempt to strengthen the weaknesses and make adequate use of a person’s strengths. In this approach resides the seeds for personal growth and excellence.

There are also programmers who excel in certain areas - conceptual creativity, ability in problem identification, analysis and solving, speed, ingenuity of design and of making best use of the available tools, etc. Such programmers, as Randall Stross formulates it, “are an order of magnitude better” than others. The experience and skills harnessed with intelligence have this transformational power that is achievable by each programmer in time.

Even if we can’t always avoid such labeling, it’s important to become aware of the latent force the labels carry with them, the effect they have on our colleagues and teammates. A label can easily act as a boomerang, hitting us back long after it was thrown away.

® Originally published on sql-troubles




IT professional/blogger with more than 24 years experience in IT - Software Engineering, BI & Analytics, Data, Project, Quality, Database & Knowledge Management